The relationship between the copy number profiles and clinical biomarkers are often known to be very complicated, especially in a heterogeneous disease such as cancer. Such a problem is further complicated by the serial correlation and measurement error present in the copy number measurements. In this talk, we consider a semiparametric functional regression model that relates a continuous outcome of interest (e.g., measurements on beta2-microblobulin) to copy number profile observed with measurement errors, adjusting for other demographic covariates. In this framework, we develop a statistical procedure to test for association between the observed copy number profiles and the outcome of interest, while accounting for possibly complex nonlinear underlying relationships and interactions. We investigate the finite sample performance of our procedure via a simulation study, and illustrate our by analyzing a Multiple Myeloma data set collected by the Multiple Myeloma Research Consortium (MMRC).